__credits__ = ["Kallinteris-Andreas"]

import numpy as np

from gymnasium import utils
from gymnasium.envs.mujoco import MujocoEnv
from gymnasium.spaces import Box


DEFAULT_CAMERA_CONFIG = {
    "trackbodyid": 2,
    "distance": 4.0,
    "lookat": np.array((0.0, 0.0, 1.15)),
    "elevation": -20.0,
}


class Walker2dEnv(MujocoEnv, utils.EzPickle):
    r"""
    ## Description
    This environment builds on the [hopper](https://gymnasium.farama.org/environments/mujoco/hopper/) environment by adding another set of legs that allow the robot to walk forward instead of hop.
    Like other MuJoCo environments, this environment aims to increase the number of independent state and control variables compared to classical control environments.
    The walker is a two-dimensional bipedal robot consisting of seven main body parts - a single torso at the top (with the two legs splitting after the torso), two thighs in the middle below the torso, two legs below the thighs, and two feet attached to the legs on which the entire body rests.
    The goal is to walk in the forward (right) direction by applying torque to the six hinges connecting the seven body parts.


    ## Action Space
    ```{figure} action_space_figures/walker2d.png
    :name: walker2d
    ```

    The action space is a `Box(-1, 1, (6,), float32)`. An action represents the torques applied at the hinge joints.

    | Num | Action                                 | Control Min | Control Max | Name (in corresponding XML file) | Joint | Type (Unit)  |
    |-----|----------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
    | 0   | Torque applied on the thigh rotor      | -1          | 1           | thigh_joint                      | hinge | torque (N m) |
    | 1   | Torque applied on the leg rotor        | -1          | 1           | leg_joint                        | hinge | torque (N m) |
    | 2   | Torque applied on the foot rotor       | -1          | 1           | foot_joint                       | hinge | torque (N m) |
    | 3   | Torque applied on the left thigh rotor | -1          | 1           | thigh_left_joint                 | hinge | torque (N m) |
    | 4   | Torque applied on the left leg rotor   | -1          | 1           | leg_left_joint                   | hinge | torque (N m) |
    | 5   | Torque applied on the left foot rotor  | -1          | 1           | foot_left_joint                  | hinge | torque (N m) |


    ## Observation Space
    The observation space consists of the following parts (in order):

    - *qpos (8 elements by default):* Position values of the robot's body parts.
    - *qvel (9 elements):* The velocities of these individual body parts (their derivatives).

    By default, the observation does not include the robot's x-coordinate (`rootx`).
    This can be included by passing `exclude_current_positions_from_observation=False` during construction.
    In this case, the observation space will be a `Box(-Inf, Inf, (18,), float64)`, where the first observation element is the x-coordinate of the robot.
    Regardless of whether `exclude_current_positions_from_observation` is set to `True` or `False`, the x-coordinate are returned in `info` with the keys `"x_position"` and `"y_position"`, respectively.

    By default, however, the observation space is a `Box(-Inf, Inf, (17,), float64)` where the elements are as follows:

    | Num | Observation                                        | Min  | Max | Name (in corresponding XML file) | Joint | Type (Unit)              |
    | --- | -------------------------------------------------- | ---- | --- | -------------------------------- | ----- | ------------------------ |
    | 0   | z-coordinate of the torso (height of Walker2d)     | -Inf | Inf | rootz                            | slide | position (m)             |
    | 1   | angle of the torso                                 | -Inf | Inf | rooty                            | hinge | angle (rad)              |
    | 2   | angle of the thigh joint                           | -Inf | Inf | thigh_joint                      | hinge | angle (rad)              |
    | 3   | angle of the leg joint                             | -Inf | Inf | leg_joint                        | hinge | angle (rad)              |
    | 4   | angle of the foot joint                            | -Inf | Inf | foot_joint                       | hinge | angle (rad)              |
    | 5   | angle of the left thigh joint                      | -Inf | Inf | thigh_left_joint                 | hinge | angle (rad)              |
    | 6   | angle of the left leg joint                        | -Inf | Inf | leg_left_joint                   | hinge | angle (rad)              |
    | 7   | angle of the left foot joint                       | -Inf | Inf | foot_left_joint                  | hinge | angle (rad)              |
    | 8   | velocity of the x-coordinate of the torso          | -Inf | Inf | rootx                            | slide | velocity (m/s)           |
    | 9   | velocity of the z-coordinate (height) of the torso | -Inf | Inf | rootz                            | slide | velocity (m/s)           |
    | 10  | angular velocity of the angle of the torso         | -Inf | Inf | rooty                            | hinge | angular velocity (rad/s) |
    | 11  | angular velocity of the thigh hinge                | -Inf | Inf | thigh_joint                      | hinge | angular velocity (rad/s) |
    | 12  | angular velocity of the leg hinge                  | -Inf | Inf | leg_joint                        | hinge | angular velocity (rad/s) |
    | 13  | angular velocity of the foot hinge                 | -Inf | Inf | foot_joint                       | hinge | angular velocity (rad/s) |
    | 14  | angular velocity of the thigh hinge                | -Inf | Inf | thigh_left_joint                 | hinge | angular velocity (rad/s) |
    | 15  | angular velocity of the leg hinge                  | -Inf | Inf | leg_left_joint                   | hinge | angular velocity (rad/s) |
    | 16  | angular velocity of the foot hinge                 | -Inf | Inf | foot_left_joint                  | hinge | angular velocity (rad/s) |
    | excluded | x-coordinate of the torso                     | -Inf | Inf | rootx                            | slide | position (m)             |


    ## Rewards
    The total reward is: ***reward*** *=* *healthy_reward bonus + forward_reward - ctrl_cost*.

    - *healthy_reward*:
    Every timestep that the Walker2d is alive, it receives a fixed reward of value `healthy_reward` (default is $1$),
    - *forward_reward*:
    A reward for moving forward,
    this reward would be positive if the Swimmer moves forward (in the positive $x$ direction / in the right direction).
    $w_{forward} \times \frac{dx}{dt}$, where
    $dx$ is the displacement of the (front) "tip" ($x_{after-action} - x_{before-action}$),
    $dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $4$),
    and `frametime` which is $0.002$ - so the default is $dt = 4 \times 0.002 = 0.008$,
    $w_{forward}$ is the `forward_reward_weight` (default is $1$).
    - *ctrl_cost*:
    A negative reward to penalize the Walker2d for taking actions that are too large.
    $w_{control} \times \|action\|_2^2$,
    where $w_{control}$ is `ctrl_cost_weight` (default is $10^{-3}$).

    `info` contains the individual reward terms.


    ## Starting State
    The initial position state is $[0, 1.25, 0, 0, 0, 0, 0, 0, 0] + \mathcal{U}_{[-reset\_noise\_scale \times I_{9}, reset\_noise\_scale \times I_{9}]}$.
    The initial velocity state is $\mathcal{U}_{[-reset\_noise\_scale \times I_{9}, reset\_noise\_scale \times I_{9}]}$.

    where $\mathcal{U}$ is the multivariate uniform continuous distribution.

    Note that the z-coordinate is non-zero so that the Walker2d can stand up immediately.


    ## Episode End
    ### Termination
    If `terminate_when_unhealthy is True` (which is the default), the environment terminates when the Walker2d is unhealthy.
    The Walker2d is unhealthy if any of the following happens:

    1. Any of the state space values is no longer finite
    2. The z-coordinate of the torso (the height) is **not** in the closed interval given by the `healthy_z_range` argument (default to $[0.8, 2.0]$).
    3. The absolute value of the angle (`observation[1]` if `exclude_current_positions_from_observation=False`, else `observation[2]`) is ***not*** in the closed interval specified by the `healthy_angle_range` argument (default is $[-1, 1]$).

    ### Truncation
    The default duration of an episode is 1000 timesteps.


    ## Arguments
    Walker2d provides a range of parameters to modify the observation space, reward function, initial state, and termination condition.
    These parameters can be applied during `gymnasium.make` in the following way:

    ```python
    import gymnasium as gym
    env = gym.make('Walker2d-v5', ctrl_cost_weight=1e-3, ...)
    ```

    | Parameter                                    | Type      | Default           | Description                                                                                                                                                                                         |
    | -------------------------------------------- | --------- | ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
    | `xml_file`                                   | **str**   |`"walker2d_v5.xml"`| Path to a MuJoCo model                                                                                                                                                                              |
    | `forward_reward_weight`                      | **float** | `1`               | Weight for _forward_reward_ term (see `Rewards` section)                                                                                                                                            |
    | `ctrl_cost_weight`                           | **float** | `1e-3`            | Weight for _ctr_cost_ term (see `Rewards` section)                                                                                                                                                  |
    | `healthy_reward`                             | **float** | `1`               | Weight for _healthy_reward_ reward (see `Rewards` section)                                                                                                                                          |
    | `terminate_when_unhealthy`                   | **bool**  | `True`            | If True, issue a `terminated` signal is unhealthy (see `Episode End` section)                                                                                                                          |
    | `healthy_z_range`                            | **tuple** | `(0.8, 2)`        | The z-coordinate of the torso of the walker must be in this range to be considered healthy (see `Episode End` section)                                                                              |
    | `healthy_angle_range`                        | **tuple** | `(-1, 1)`         | The angle must be in this range to be considered healthy (see `Episode End` section)                                                                                                                |
    | `reset_noise_scale`                          | **float** | `5e-3`            | Scale of random perturbations of initial position and velocity (see `Starting State` section)                                                                                                       |
    | `exclude_current_positions_from_observation` | **bool**  | `True`            | Whether or not to omit the x-coordinate from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies (see `Observation Space` section) |


    ## Version History
    * v5:
        - Minimum `mujoco` version is now 2.3.3.
        - Added support for fully custom/third party `mujoco` models using the `xml_file` argument (previously only a few changes could be made to the existing models).
        - Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments.
        - Added `env.observation_structure`, a dictionary for specifying the observation space compose (e.g. `qpos`, `qvel`), useful for building tooling and wrappers for the MuJoCo environments.
        - Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`.
        - Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages.
        - In v2, v3 and v4 the models have different friction values for the two feet (left foot friction == 1.9 and right foot friction == 0.9). The `Walker-v5` model is updated to have the same friction for both feet (set to 1.9). This causes the Walker2d's the right foot to slide less on the surface and therefore require more force to move (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/477)).
        - Fixed bug: `healthy_reward` was given on every step (even if the Walker2D is unhealthy), now it is only given if the Walker2d is healthy. The `info` "reward_survive" is updated with this change (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/526)).
        - Restored the `xml_file` argument (was removed in `v4`).
        - Added individual reward terms in `info` (`info["reward_forward"]`, `info["reward_ctrl"]`, `info["reward_survive"]`).
        - Added `info["z_distance_from_origin"]` which is equal to the vertical distance of the "torso" body from its initial position.
    * v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
    * v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
    * v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
    * v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
    * v0: Initial versions release
    """

    metadata = {
        "render_modes": [
            "human",
            "rgb_array",
            "depth_array",
            "rgbd_tuple",
        ],
    }

    def __init__(
        self,
        xml_file: str = "walker2d_v5.xml",
        frame_skip: int = 4,
        default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
        forward_reward_weight: float = 1.0,
        ctrl_cost_weight: float = 1e-3,
        healthy_reward: float = 1.0,
        terminate_when_unhealthy: bool = True,
        healthy_z_range: tuple[float, float] = (0.8, 2.0),
        healthy_angle_range: tuple[float, float] = (-1.0, 1.0),
        reset_noise_scale: float = 5e-3,
        exclude_current_positions_from_observation: bool = True,
        **kwargs,
    ):
        utils.EzPickle.__init__(
            self,
            xml_file,
            frame_skip,
            default_camera_config,
            forward_reward_weight,
            ctrl_cost_weight,
            healthy_reward,
            terminate_when_unhealthy,
            healthy_z_range,
            healthy_angle_range,
            reset_noise_scale,
            exclude_current_positions_from_observation,
            **kwargs,
        )

        self._forward_reward_weight = forward_reward_weight
        self._ctrl_cost_weight = ctrl_cost_weight

        self._healthy_reward = healthy_reward
        self._terminate_when_unhealthy = terminate_when_unhealthy

        self._healthy_z_range = healthy_z_range
        self._healthy_angle_range = healthy_angle_range

        self._reset_noise_scale = reset_noise_scale

        self._exclude_current_positions_from_observation = (
            exclude_current_positions_from_observation
        )

        MujocoEnv.__init__(
            self,
            xml_file,
            frame_skip,
            observation_space=None,
            default_camera_config=default_camera_config,
            **kwargs,
        )

        self.metadata = {
            "render_modes": [
                "human",
                "rgb_array",
                "depth_array",
                "rgbd_tuple",
            ],
            "render_fps": int(np.round(1.0 / self.dt)),
        }

        obs_size = (
            self.data.qpos.size
            + self.data.qvel.size
            - exclude_current_positions_from_observation
        )
        self.observation_space = Box(
            low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64
        )

        self.observation_structure = {
            "skipped_qpos": 1 * exclude_current_positions_from_observation,
            "qpos": self.data.qpos.size
            - 1 * exclude_current_positions_from_observation,
            "qvel": self.data.qvel.size,
        }

    @property
    def healthy_reward(self):
        return self.is_healthy * self._healthy_reward

    def control_cost(self, action):
        control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
        return control_cost

    @property
    def is_healthy(self):
        z, angle = self.data.qpos[1:3]

        min_z, max_z = self._healthy_z_range
        min_angle, max_angle = self._healthy_angle_range

        healthy_z = min_z < z < max_z
        healthy_angle = min_angle < angle < max_angle
        is_healthy = healthy_z and healthy_angle

        return is_healthy

    def _get_obs(self):
        position = self.data.qpos.flatten()
        velocity = np.clip(self.data.qvel.flatten(), -10, 10)

        if self._exclude_current_positions_from_observation:
            position = position[1:]

        observation = np.concatenate((position, velocity)).ravel()
        return observation

    def step(self, action):
        x_position_before = self.data.qpos[0]
        self.do_simulation(action, self.frame_skip)
        x_position_after = self.data.qpos[0]
        x_velocity = (x_position_after - x_position_before) / self.dt

        observation = self._get_obs()
        reward, reward_info = self._get_rew(x_velocity, action)
        terminated = (not self.is_healthy) and self._terminate_when_unhealthy
        info = {
            "x_position": x_position_after,
            "z_distance_from_origin": self.data.qpos[1] - self.init_qpos[1],
            "x_velocity": x_velocity,
            **reward_info,
        }

        if self.render_mode == "human":
            self.render()
        # truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
        return observation, reward, terminated, False, info

    def _get_rew(self, x_velocity: float, action):
        forward_reward = self._forward_reward_weight * x_velocity
        healthy_reward = self.healthy_reward
        rewards = forward_reward + healthy_reward

        ctrl_cost = self.control_cost(action)
        costs = ctrl_cost
        reward = rewards - costs

        reward_info = {
            "reward_forward": forward_reward,
            "reward_ctrl": -ctrl_cost,
            "reward_survive": healthy_reward,
        }

        return reward, reward_info

    def reset_model(self):
        noise_low = -self._reset_noise_scale
        noise_high = self._reset_noise_scale

        qpos = self.init_qpos + self.np_random.uniform(
            low=noise_low, high=noise_high, size=self.model.nq
        )
        qvel = self.init_qvel + self.np_random.uniform(
            low=noise_low, high=noise_high, size=self.model.nv
        )

        self.set_state(qpos, qvel)

        observation = self._get_obs()
        return observation

    def _get_reset_info(self):
        return {
            "x_position": self.data.qpos[0],
            "z_distance_from_origin": self.data.qpos[1] - self.init_qpos[1],
        }
